Machine learning algorithms' application to predict childhood vaccination among children aged 12-23 months in Ethiopia: Evidence 2016 Ethiopian Demographic and Health Survey dataset.

ABSTRACT:

Introduction

Childhood vaccination is a cost-effective public health intervention to reduce child mortality and morbidity. But, vaccination coverage remains low, and previous similar studies have not focused on machine learning algorithms to predict childhood vaccination. Therefore, knowledge extraction, association rule formulation, and discovering insights from hidden patterns in vaccination data are limited. Therefore, this study aimed to predict childhood vaccination among children aged 12-23 months using the best machine learning algorithm.

Methods

A cross-sectional study design with a two-stage sampling technique was used. A total of 1617 samples of living children aged 12-23 months were used from the 2016 Ethiopian Demographic and Health Survey dataset. The data was p

SUBMITTER: Demsash AW 

PROVIDER: S-EPMC10584162 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature